Get OD data from IMOB

The biclaR project hosts several open datasets useful for this spatial analysis relateg with transportation in the Lisbon Metro region.

TRIPSall = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/TRIPSmode_municipal.Rds"))

TRIPSall[TRIPSall == "Setubal"] = "Setúbal" #rename

knitr::kable(head(TRIPSall)) # have a look at data
Origem Destino modo viagens
Alcochete Alcochete Bike 320.54
Alcochete Alcochete Car 12432.32
Alcochete Alcochete Motorcycle 52.12
Alcochete Alcochete Other 6.85
Alcochete Alcochete Transit 834.06
Alcochete Alcochete Walk 6914.64

Remove intrazonal trips and group by Private vs Public

# With all modes
TRIPSinter = TRIPSall |>
  filter(Origem != Destino) |> # remove internal trips
  as.data.frame() |> 
  group_by(Origem, Destino) |> 
  summarise(viagens = sum(viagens)) |> 
  ungroup() |> 
  od_oneway() # sum the O with D - doesn't matter the start and end point for vizualization purpouses

  
# With variables of public or private modes
TRIPSvs = TRIPSall |>
  filter(Origem != Destino) |> # remove internal trips
  as.data.frame() |> 
  mutate(Tipo = recode(modo,  # recode in TI and TC
         Bike = "TIndividual",
         Car = "TIndividual",
         Motorcycle = "TIndividual",
         Other = "TIndividual",
         Transit = "TColetivo",
         Walk = "TIndividual")) |> 
  group_by(Origem, Destino, Tipo) |> 
  summarise(viagens = sum(viagens)) |> 
  ungroup() |> 
  pivot_wider(names_from = Tipo,
              values_from = viagens)

TRIPSvs[is.na(TRIPSvs)] = 0 # há casos em que não há viagens registadas entre municípios
TRIPSvs = od_oneway(TRIPSvs) # magia para somar O-D com D-O


knitr::kable(head(TRIPSvs)) # viagens separadas por TI e TC
Origem Destino TIndividual TColetivo
Alcochete Almada 1277.85 48.88
Alcochete Amadora 215.71 162.54
Alcochete Barreiro 1710.42 10.28
Alcochete Cascais 228.50 0.00
Alcochete Lisboa 4036.93 1612.31
Alcochete Loures 948.36 0.00

A total of 5.3 million trips are made daily in the AML, of which 1823 thousand are intercity.

Get AML maps

MUNICIPIOSgeo = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/MUNICIPIOSgeo.Rds"))
MUNICIPIOSgeo$Concelho[MUNICIPIOSgeo$Concelho == "Setubal"] = "Setúbal"

AML_dtcc = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/ZONAMENTO_imob.Rds"))
AML_dtcc = AML_dtcc |> select(DTMN, DTMN_DSG) |> unique()

CENTROIDS = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/CENTROIDS_municipios.Rds"))
CENTROIDS = CENTROIDS |>
  left_join(AML_dtcc, by = c("DTMN11" = "DTMN")) |>
  select(DTMN_DSG, geometry) |>
  filter(DTMN_DSG != "Montijo")

MONTIJO = sf::st_read("geo/Montijo.geojson", quiet = TRUE) # Porque o centro das duas parts do Montijo não calha dentro do Montijo
CENTROIDS = rbind(CENTROIDS, MONTIJO)

Make maps

Use stplanr to create the desire lines.

LINHASod = od2line(flow = TRIPSinter,
                   zones = CENTROIDS,
                   zone_code = "DTMN_DSG") |> 
  filter(viagens >= 1000)
LINHASod_noLX = LINHASod |> filter(Origem != "Lisboa" & Destino != "Lisboa")

LINHASod_TI = od2line(flow = TRIPSvs |> select(-TColetivo),
                      zones = CENTROIDS,
                      zone_code = "DTMN_DSG") |> 
  filter(TIndividual > 1000)
LINHASod_TI_noLX = LINHASod_TI |> filter(Origem != "Lisboa" & Destino != "Lisboa")

LINHASod_TC = od2line(flow = TRIPSvs |> select(-TIndividual),
                      zones = CENTROIDS,
                      zone_code = "DTMN_DSG") |> 
  filter(TColetivo > 1000)
LINHASod_TC_noLX = LINHASod_TC |> filter(Origem != "Lisboa" & Destino != "Lisboa")

All Trips

Including Lisbon

m1 =  tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod %>% filter(viagens >=5000)) +
  tm_lines("viagens",
           palette = viridis::magma(n = 4, direction = -1),
           breaks = c(5000, 10000, 50000, 100000, 161000),
           lwd = "viagens",
           scale = 30,
           title.col = "Viagens intermunicipais") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m1

Excluding Lisbon

m2 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_noLX %>% filter(viagens >=3000)) +
  tm_lines("viagens",
           palette = viridis::magma(n = 4, direction = -1),
           breaks = c(3000, 10000, 30000, 60000, 73000),
           lwd = "viagens",
           scale = 30,
           title.col = "Viagens intermunicipais, fora de Lisboa") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m2

Only Public Transit trips

Including Lisbon

m3 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TC %>% filter(TColetivo >=3000)) +
  tm_lines("TColetivo",
           palette = viridis::viridis(n = 4, direction = -1),
           breaks = c(3000, 5000, 10000, 25000, 50000),
           lwd = "TColetivo",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Coletivo") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m3

Excluding Lisbon

m4 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TC_noLX %>% filter(TColetivo >=2000)) +
  tm_lines("TColetivo",
           palette = viridis::viridis(n = 4, direction = -1),
           breaks = c(2000, 4000, 7000, 10000, 13000),
           lwd = "TColetivo",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Coletivo (fora Lx)") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m4

Only Private trips

Including Lisbon

m5 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TI %>% filter(TIndividual >=5000)) +
  tm_lines("TIndividual",
           palette = viridis::plasma(n = 4, direction = -1),
           breaks = c(5000, 20000, 50000, 100000, 115000),
           lwd = "TIndividual",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Individual") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m5

Excluding Lisbon

m6 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TI_noLX %>% filter(TIndividual >=3000)) +
  tm_lines("TIndividual",
           palette = viridis::plasma(n = 4, direction = -1),
           breaks = c(3000, 10000, 25000, 50000, 65000),
           lwd = "TIndividual",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Individual (fora Lx)") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m6
---
title: "Trips in AML with maps"
author: "R Félix"
date: "MQAT 2023"
output:
  html_document:
    toc: true
    toc_depth: 4
    toc_float: true
    code_folding: "hide"
    code_download: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
                      message = FALSE,
                      warning = FALSE)
```

```{r libraries, message=FALSE, warning=FALSE, include=FALSE}
library(tidyverse)
library(stplanr)
library(tmap)
tmap_mode("view")
```


## Get OD data from IMOB

The [**biclaR**](https://biclar.tmlmobilidade.pt/) project hosts several [open datasets](https://github.com/U-Shift/biclar/releases/tag/0.0.1) useful for this spatial analysis relateg with transportation in the Lisbon Metro region.

```{r readdata}
TRIPSall = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/TRIPSmode_municipal.Rds"))

TRIPSall[TRIPSall == "Setubal"] = "Setúbal" #rename

knitr::kable(head(TRIPSall)) # have a look at data
```

Remove **intrazonal** trips and group by Private vs Public

```{r tripsclean}
# With all modes
TRIPSinter = TRIPSall |>
  filter(Origem != Destino) |> # remove internal trips
  as.data.frame() |> 
  group_by(Origem, Destino) |> 
  summarise(viagens = sum(viagens)) |> 
  ungroup() |> 
  od_oneway() # sum the O with D - doesn't matter the start and end point for vizualization purpouses

  
# With variables of public or private modes
TRIPSvs = TRIPSall |>
  filter(Origem != Destino) |> # remove internal trips
  as.data.frame() |> 
  mutate(Tipo = recode(modo,  # recode in TI and TC
         Bike = "TIndividual",
         Car = "TIndividual",
         Motorcycle = "TIndividual",
         Other = "TIndividual",
         Transit = "TColetivo",
         Walk = "TIndividual")) |> 
  group_by(Origem, Destino, Tipo) |> 
  summarise(viagens = sum(viagens)) |> 
  ungroup() |> 
  pivot_wider(names_from = Tipo,
              values_from = viagens)

TRIPSvs[is.na(TRIPSvs)] = 0 # há casos em que não há viagens registadas entre municípios
TRIPSvs = od_oneway(TRIPSvs) # magia para somar O-D com D-O


knitr::kable(head(TRIPSvs)) # viagens separadas por TI e TC
```

A total of `r round(sum(TRIPSall$viagens)/1000000,1)` million trips are made daily in the AML, of which `r round(sum(TRIPSinter$viagens)/1000)` thousand are intercity.

## Get AML maps

```{r amlgeo}
MUNICIPIOSgeo = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/MUNICIPIOSgeo.Rds"))
MUNICIPIOSgeo$Concelho[MUNICIPIOSgeo$Concelho == "Setubal"] = "Setúbal"

AML_dtcc = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/ZONAMENTO_imob.Rds"))
AML_dtcc = AML_dtcc |> select(DTMN, DTMN_DSG) |> unique()

CENTROIDS = readRDS(url("https://github.com/U-Shift/biclar/releases/download/0.0.1/CENTROIDS_municipios.Rds"))
CENTROIDS = CENTROIDS |>
  left_join(AML_dtcc, by = c("DTMN11" = "DTMN")) |>
  select(DTMN_DSG, geometry) |>
  filter(DTMN_DSG != "Montijo")

MONTIJO = sf::st_read("geo/Montijo.geojson", quiet = TRUE) # Porque o centro das duas parts do Montijo não calha dentro do Montijo
CENTROIDS = rbind(CENTROIDS, MONTIJO)
```

## Make maps

Use `stplanr` to create the [desire lines](https://docs.ropensci.org/stplanr/reference/od2line.html).

```{r od2line}
LINHASod = od2line(flow = TRIPSinter,
                   zones = CENTROIDS,
                   zone_code = "DTMN_DSG") |> 
  filter(viagens >= 1000)
LINHASod_noLX = LINHASod |> filter(Origem != "Lisboa" & Destino != "Lisboa")

LINHASod_TI = od2line(flow = TRIPSvs |> select(-TColetivo),
                      zones = CENTROIDS,
                      zone_code = "DTMN_DSG") |> 
  filter(TIndividual > 1000)
LINHASod_TI_noLX = LINHASod_TI |> filter(Origem != "Lisboa" & Destino != "Lisboa")

LINHASod_TC = od2line(flow = TRIPSvs |> select(-TIndividual),
                      zones = CENTROIDS,
                      zone_code = "DTMN_DSG") |> 
  filter(TColetivo > 1000)
LINHASod_TC_noLX = LINHASod_TC |> filter(Origem != "Lisboa" & Destino != "Lisboa")
```

### All Trips

#### Including Lisbon

```{r intermunicipaisALL, out.width = '100%'}
m1 =  tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod %>% filter(viagens >=5000)) +
  tm_lines("viagens",
           palette = viridis::magma(n = 4, direction = -1),
           breaks = c(5000, 10000, 50000, 100000, 161000),
           lwd = "viagens",
           scale = 30,
           title.col = "Viagens intermunicipais") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m1
```

#### Excluding Lisbon

```{r intermunicipaissemLX, out.width = '100%'}
m2 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_noLX %>% filter(viagens >=3000)) +
  tm_lines("viagens",
           palette = viridis::magma(n = 4, direction = -1),
           breaks = c(3000, 10000, 30000, 60000, 73000),
           lwd = "viagens",
           scale = 30,
           title.col = "Viagens intermunicipais, fora de Lisboa") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m2
```

### Only Public Transit trips

#### Including Lisbon

```{r intermunicipais_TC, out.width = '100%'}
m3 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TC %>% filter(TColetivo >=3000)) +
  tm_lines("TColetivo",
           palette = viridis::viridis(n = 4, direction = -1),
           breaks = c(3000, 5000, 10000, 25000, 50000),
           lwd = "TColetivo",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Coletivo") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m3
```

#### Excluding Lisbon

```{r intermunicipaissemLX_TC, out.width = '100%'}
m4 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TC_noLX %>% filter(TColetivo >=2000)) +
  tm_lines("TColetivo",
           palette = viridis::viridis(n = 4, direction = -1),
           breaks = c(2000, 4000, 7000, 10000, 13000),
           lwd = "TColetivo",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Coletivo (fora Lx)") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m4
```


### Only Private trips

#### Including Lisbon

```{r intermunicipais_TI, out.width = '100%'}
m5 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TI %>% filter(TIndividual >=5000)) +
  tm_lines("TIndividual",
           palette = viridis::plasma(n = 4, direction = -1),
           breaks = c(5000, 20000, 50000, 100000, 115000),
           lwd = "TIndividual",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Individual") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m5
```

#### Excluding Lisbon

```{r intermunicipaissemLX_TI, out.width = '100%'}
m6 = tm_shape(MUNICIPIOSgeo) +
  tm_borders(col = "grey") +
  tm_shape(LINHASod_TI_noLX %>% filter(TIndividual >=3000)) +
  tm_lines("TIndividual",
           palette = viridis::plasma(n = 4, direction = -1),
           breaks = c(3000, 10000, 25000, 50000, 65000),
           lwd = "TIndividual",
           scale = 30,
           title.col = "Viagens intermunicipais, em Transporte Individual (fora Lx)") +
  tm_shape(CENTROIDS) +
  tm_text("DTMN_DSG", size = 0.8, col = "gray25")
m6
```
